Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy Structure Adjustment?

Ying Wang, Peipei Shang, Lichun He, Yingchun Zhang and Dandan Liu
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Ying Wang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Peipei Shang: Editorial Department, Dongbei University of Finance and Economics, Dalian 116025, China
Lichun He: School of Public Administration, Dongbei University of Finance and Economics, Dalian 116025, China
Yingchun Zhang: School of Economics, Qingdao University, Qingdao 266071, China
Dandan Liu: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China

Energies, 2018, vol. 11, issue 10, 1-32

Abstract: To mitigate global warming, the Chinese government has successively set carbon intensity targets for 2020 and 2030. Energy restructuring is critical for achieving these targets. In this paper, a combined forecasting model is utilized to predict primary energy consumption in China. Subsequently, the Markov model and non-linear programming model are used to forecast China’s energy structure in 2020 and 2030 in three scenarios. Carbon intensities were forecasted by combining primary energy consumption, energy structure and economic forecasting. Finally, this paper analyzes the contribution potential of energy structure optimization in each scenario. Our main research conclusions are that in 2020, the optimal energy structure will enable China to achieve its carbon intensity target under the conditions of the unconstrained scenario, policy-constrained scenario and minimum external costs of carbon emissions scenario. Under the three scenarios, the carbon intensity will decrease by 42.39%, 43.74%, and 42.67%, respectively, relative to 2005 levels. However, in 2030, energy structure optimization cannot fully achieve China’s carbon intensity target under any of the three scenarios. It is necessary to undertake other types of energy-saving emission reduction measures. Thus, our paper concludes with some policy suggestions to further mitigate China’s carbon intensities.

Keywords: carbon intensity target; energy structure; gray model (GM (1, 1)); generalized regression neural network (GRNN); Markov forecasting model; non-linear programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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